Logic and Probabilistic Circuits - Lecture 1
Explore logical reasoning and probabilistic modeling techniques, focusing on their applications in database theory and artificial intelligence.
An Approximate Skolem Function Counter
Explores an algorithm for counting Skolem functions in quantified Boolean formulas, addressing challenges in synthesis problems and providing PAC guarantees for approximate counting.
Compiling FO Sentences to Circuits - Upper Bounds and Lower Bounds on the Size of the Circuit
Explores compilation of Boolean formulas into circuits, focusing on provenance of FO sentences. Examines size complexity and limitations of various circuit types for model counting.
Tractable Bounding of Counterfactual Queries by Knowledge Compilation
Explores bounding partially identifiable queries in causal models using knowledge compilation, offering computational advantages for inference in Bayesian networks with shared structures.
Logic and Probabilistic Circuits - Lecture 4
Explore advanced concepts in logic and probabilistic circuits, focusing on their applications in database theory and artificial intelligence.
Logic and Probabilistic Circuits - Part 3
Explore advanced concepts in logic and probabilistic circuits, focusing on their applications in database theory and AI algorithms.
Logic and Probabilistic Circuits - Part 2
Explore advanced concepts in logic and probabilistic circuits, focusing on their applications in database theory and AI algorithms.
Approximating the TV Distance Between Two Product Distributions
Explore techniques for approximating total variation distance between high-dimensional product distributions, including randomized coupling and deterministic sparsification methods.
On Finding Modes of Gaussian Sum-Product Networks
Explore challenges and tractability of finding modes in Probabilistic Circuits, focusing on Gaussian Sum-Product Networks for machine learning tasks like structured prediction.
Marginal Determinism in Structured Decomposable Circuits
Explores marginal determinism in probabilistic circuits, introducing md-vtrees for structured decomposable PCs. Discusses tractability conditions, efficient algorithms, and applications to causal inference queries like backdoor adjustment.
Subtractive Mixture Models: Representation and Learning
Exploring subtractive mixtures in probabilistic circuits: increased expressiveness, efficient learning, and inference for complex distributions. Theoretical and empirical advantages over traditional additive mixtures.
Solving Marginal MAP Exactly by Probabilistic Circuit Transformations
Efficient marginal MAP solver for probabilistic circuits using iterative transformations and pruning, enabling exact solutions without search for decision-making problems.
Leveraging Tree Automata Approximation Results for Efficient Sampling and Counting
Explore efficient approximation algorithms for counting and sampling satisfying assignments in structured DNNF circuits, leveraging recent tree automata results.
Combined Approximations for Probabilistic Query Evaluation - An Intensional Approach
Explore innovative techniques for probabilistic query evaluation using combined approximations, focusing on an intensional approach to enhance efficiency and accuracy.
First-Order Model Counting and Sampling
Explores recent developments in weighted first-order model counting and sampling, discussing applications in combinatorics, probability theory, and potential for declarative frameworks in sampling combinatorial structures.
Model Counting Meets Distinct Estimation
Exploring connections between model counting and F0 estimation, revealing shared techniques and developing new algorithms for both fields through cross-domain insights.
On Inference and Learning With Probabilistic Generating Circuits
Explore efficient inference algorithms for Probabilistic Generating Circuits, addressing computational challenges and discussing implications for learning from data in probabilistic modeling.
Probabilistic Flow Circuits: Towards Deep Models for Tractable Inference - Oral Session 6
Explore a novel approach combining probabilistic circuits with normalizing flows for enhanced expressivity while maintaining tractable inference in deep probabilistic models.
Probabilistic Circuits That Know What They Don't Know - UAI Oral Session 6
Explore probabilistic circuits' robustness to out-of-distribution data and learn about tractable dropout inference for improved uncertainty quantification in a single forward pass.
SLASH: Probabilistic Circuits - Part 1
Explore probabilistic circuits in the SLASH framework. Learn their importance and applications in neuro-symbolic AI and machine learning.
Probabilistic Circuits: Deep Probabilistic Models with Tractable Inference
Explore probabilistic circuits as deep models for efficient reasoning under uncertainty, examining their structural properties and connections to neuro-symbolic AI.